To address the issues of high miss rates for small targets, severe interference from complex backgrounds, and the inability of existing models to balance accuracy and efficiency in infrared detection of thermal defects in smart electricity meters and their junction boxes, we propose a lightweight smart electricity meter target detection algorithm, YOLO-MCSL, based on an improved YOLOv8s architecture. This algorithm aims to meet the urgent need for real-time detection in power field inspections. First, the MobileNetV4 lightweight network is adopted as the backbone to significantly reduce the number of model parameters and computational overhead. Second, the CCFF cross-scale feature fusion module from the RT-DETR model is introduced to enhance the detection capability for multi-scale small thermal defects. Subsequently, a lightweight C2f_Star module is designed to replace the original C2f structure, further compressing the model and improving feature extraction efficiency. Additionally, we construct the LSCD lightweight shared convolution detection head, which reduces redundant computation through parameter sharing. Furthermore, we combine the Focaler-SIoU loss function to optimize the bounding box regression process, enhancing the differentiation between easy and hard samples. Finally, we apply a layer-wise adaptive amplitude pruning algorithm to structurally prune the model, achieving a balance between performance and lightweight design. Experiments were conducted on a selfconstructed infrared image dataset of thermal defects in smart electricity meters. The results show that in the detection of three key components—junction boxes, battery modules, and displays—the detection accuracy of YOLO-MCSL reached 91.6%, 99.2%, and 99.5%, respectively, with an overall mAP@0.5 of 97.9%. Compared to the YOLOv8s baseline model, the number of parameters was reduced to 1.749 M (a reduction of 84.3 %), computational complexity was reduced to 5.7 GFLOPs (a reduction of 80.2%), and model memory usage was reduced to 3.8 MB (a reduction of 82.3%). This method provides a high-precision, lightweight, and embeddable solution for smart electricity meter defect detection, showing promising prospects for engineering applications.